Spatial Planning and Connectivity Corpus - Technical Background Report

IPBES Spatial Planning and Connectivity Assessment

Abstract

To Be added

Disclaimer

Contributors

Assessment Experts

• xxx, yyy

Data and Knowledge tsu

• Niamir, Aidin

Working Title

IPBES_SPC_Corpus

Code repo

Github repository

Build No: 89

Introduction

The literature search for the Spatial Planning and Connectivity assessment corpus was conducted using search terms provided by the experts and refined in co-operation with the IPBES task force for data and knowledge management. The search was conducted using OpenAlex, scripted from R to use the OpenAlex API. Search terms for the following searches were defined:

• Spatial Planning and Connectivity,
• Nature / Environment
• additional search terms for specific corpora

To assess the quality of the corpus, sets of key papers were selected by the experts to verify if these are in the corpus.

The following terminology is used in this document:

• Corpus: A body of works as based on a search on OpenAlex
• Spatial Planning and Connectivity Assessment Corpus: Short: SPC corpus; The corpus resulting from the search terms TO BE ADDED
• work: terminology used for a single document in their dataset. Each work has a unique OpenAlex id, but not necesarily a DOI.

The following searches are conducted on Title and Abstract only to account for fluctuating availability of full text searches and make the search more focussed..

Schematic Overview

Overview

flowchart TD
    Start([Start literature search]) --> SPC["spc_corpus.yaml<br/>Assemble base SPC corpus"]
    click SPC "./input/search_terms/spc_corpus.yaml" "Open spc_corpus.yaml"
    SPC --> SPC_list["spc keyword set<br/>(planning & connectivity terms)"]
    SPC --> NATURE_list["nature keyword set<br/>(environmental context terms)"]
    SPC_list --> BaseQuery["Level 1 query<br/>spc terms AND nature dictionary"]
    NATURE_list --> BaseQuery

    BaseQuery --> ChapterSelect{Apply chapter / theme refinements}

    ChapterSelect --> CH1["Chapter 1<br/>chapter_1.v1.yaml<br/>Governance & planning principles"]
    click CH1 "./input/search_terms/chapter_1.v1.yaml" "Open chapter_1.v1.yaml"
    ChapterSelect --> CH2["Chapter 2<br/>chapter_2.yaml + chapter_2_add.yaml + chapter_2_sdg.yaml<br/>GBF targets, nexus themes, SDGs"]
    click CH2 "./input/search_terms/chapter_2.yaml" "Open chapter_2.yaml"
    ChapterSelect --> CH3["Chapter 3<br/>chapter_3.yaml<br/>Restoration & conservation planning"]
    click CH3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"
    ChapterSelect --> CH4["Chapter 4<br/>chapter_4.yaml<br/>Connectivity evidence & metrics"]
    click CH4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"
    ChapterSelect --> CH5["Chapter 5<br/>chapter_5_* files<br/>Foresight & futures (sections, themes, cross-cutting)"]
    click CH5 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter 5 search terms"
    ChapterSelect --> CH6["Chapter 6<br/>chapter_6.yaml (+ chapter_6_r2.yaml optional)<br/>Enabling environment"]
    click CH6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

Chapter 1

flowchart LR
    Start([SPC Corpus]) --> Ch1["chapter_1.v1.yaml<br/>Level 2 refinements"]
    click Ch1 "./input/search_terms/chapter_1.v1.yaml" "Open chapter_1.v1.yaml"

    subgraph Chapter1Sets["Chapter 1 thematic searches"]
        direction TB
        C1_1["Set 1:<br/>land/spatial planning<br/>+ biodiversity goals<br/>+ societal needs/values"]
        C1_2["Set 2:<br/>adaptive/scenario planning<br/>+ monitoring/feedback"]
        C1_3["Set 3:<br/>evidence & precaution<br/>+ ILK knowledge base"]
        C1_4["Set 4:<br/>multilevel/transparent governance<br/>+ customary coherence"]
        C1_5["Set 5:<br/>participatory planning<br/>+ co-design / engagement"]
        C1_6["Set 6:<br/>equity / rights / tenure<br/>+ justice outcomes"]
        C1_7["Set 7:<br/>connectivity (land-sea/cross-scale)<br/>+ nexus & climate links"]
    end

    Ch1 --> C1_1
    Ch1 --> C1_2
    Ch1 --> C1_3
    Ch1 --> C1_4
    Ch1 --> C1_5
    Ch1 --> C1_6
    Ch1 --> C1_7

Chapter 2

flowchart LR
    Start([SPC Corpus]) --> Ch2L2["chapter_2.yaml<br/>Level 2 GBF bundles"]
    click Ch2L2 "../search_terms/chapter_2.yaml" "Open chapter_2.yaml"

    subgraph L1_GBF["GBF contexts & targets"]
        direction TB
        GBF_Urban["GBF-1 Urban"]
        GBF_Rural["GBF-1 Rural"]
        GBF_Fresh["GBF-1 Freshwater"]
        GBF_Marine["GBF-1 Marine"]
        GBF_Restore["GBF-2 Ecosystem restoration"]
        T3["Target 3"]
        T4["Target 4"]
        T5["Target 5"]
        T6["Target 6"]
        T7["Target 7"]
        T8["Target 8"]
        T9["Target 9"]
        T10["Target 10"]
        T11["Target 11"]
        T12["Target 12"]
        T13["Target 13"]
        T14["Target 14"]
        T15["Target 15"]
        T16["Target 16"]
        T17["Target 17"]
        T18["Target 18"]
        T19["Target 19"]
        T20["Target 20"]
        T21["Target 21"]
        T22["Target 22"]
        T23["Target 23"]
        REL["Spatial Planning Related"]
    end
    Ch2L2 --> GBF_Urban
    Ch2L2 --> GBF_Rural
    Ch2L2 --> GBF_Fresh
    Ch2L2 --> GBF_Marine
    Ch2L2 --> GBF_Restore
    Ch2L2 --> T3
    Ch2L2 --> T4
    Ch2L2 --> T5
    Ch2L2 --> T6
    Ch2L2 --> T7
    Ch2L2 --> T8
    Ch2L2 --> T9
    Ch2L2 --> T10
    Ch2L2 --> T11
    Ch2L2 --> T12
    Ch2L2 --> T13
    Ch2L2 --> T14
    Ch2L2 --> T15
    Ch2L2 --> T16
    Ch2L2 --> T17
    Ch2L2 --> T18
    Ch2L2 --> T19
    Ch2L2 --> T20
    Ch2L2 --> T21
    Ch2L2 --> T22
    Ch2L2 --> T23
    Ch2L2 --> REL

    Start --> Ch2L3["chapter_2_add.yaml<br/>Level 3 nexus themes"]
    click Ch2L3 "../search_terms/chapter_2_add.yaml" "Open chapter_2_add.yaml"
    subgraph L1_NexusSets["Nexus add-ons"]
        direction TB
        Nexus_Water["Water"]
        Nexus_Food["Food"]
        Nexus_Health["Health"]
        Nexus_Climate["Climate"]
    end
    Ch2L3 --> Nexus_Water
    Ch2L3 --> Nexus_Food
    Ch2L3 --> Nexus_Health
    Ch2L3 --> Nexus_Climate

    L1_GBF --> Ch2L4["chapter_2_sdg.yaml<br/>Level 4 SDG filters"]
    L1_NexusSets --> Ch2L4
    click Ch2L4 "../search_terms/chapter_2_sdg.yaml" "Open chapter_2_sdg.yaml"
    subgraph SDGSets["SDG goal filters"]
        direction TB
        SDG1["SDG 1"]
        SDG2["SDG 2"]
        SDG3["SDG 3"]
        SDG4["SDG 4"]
        SDG5["SDG 5"]
        SDG6["SDG 6"]
        SDG7["SDG 7"]
        SDG8["SDG 8"]
        SDG9["SDG 9"]
        SDG10["SDG 10"]
        SDG11["SDG 11"]
        SDG12["SDG 12"]
        SDG13["SDG 13"]
        SDG14["SDG 14"]
        SDG15["SDG 15"]
        SDG16["SDG 16"]
        SDG17["SDG 17"]
    end
    Ch2L4 --> SDG1
    Ch2L4 --> SDG2
    Ch2L4 --> SDG3
    Ch2L4 --> SDG4
    Ch2L4 --> SDG5
    Ch2L4 --> SDG6
    Ch2L4 --> SDG7
    Ch2L4 --> SDG8
    Ch2L4 --> SDG9
    Ch2L4 --> SDG10
    Ch2L4 --> SDG11
    Ch2L4 --> SDG12
    Ch2L4 --> SDG13
    Ch2L4 --> SDG14
    Ch2L4 --> SDG15
    Ch2L4 --> SDG16
    Ch2L4 --> SDG17

Chapter 3

flowchart LR
    Start([SPC Corpus]) --> Ch3["chapter_3.yaml<br/>Level 2 refinements"]
    click Ch3 "./input/search_terms/chapter_3.yaml" "Open chapter_3.yaml"

    subgraph Chapter3Sets["Chapter 3 searches"]
        direction TB
        C3_1["Set 1:<br/>Protected/OECM + NBSAP + cases<br/>+ spatial prioritization + regional scales"]
        C3_2["Set 2:<br/>Restoration types + inclusivity & ILK"]
        C3_3["Set 3:<br/>Restoration planning + connectivity + resilience"]
        C3_4["Set 4:<br/>Systematic conservation planning / gap analysis"]
        C3_5["Set 5:<br/>Protected area & connectivity planning"]
        C3_6["Set 6:<br/>Landscape/species/corridor networks"]
        C3_7["Set 7:<br/>Conservation planning + ecosystem services"]
        C3_8["Set 8:<br/>Participatory conservation mapping"]
        C3_9["Set 9:<br/>Conservation effectiveness + spatial planning"]
        C3_10["Set 10:<br/>Adaptive management under global change drivers"]
    end
    Ch3 --> C3_1
    Ch3 --> C3_2
    Ch3 --> C3_3
    Ch3 --> C3_4
    Ch3 --> C3_5
    Ch3 --> C3_6
    Ch3 --> C3_7
    Ch3 --> C3_8
    Ch3 --> C3_9
    Ch3 --> C3_10

Chapter 4

flowchart LR
    Start([SPC Corpus]) --> Ch4["chapter_4.yaml<br/>Level 2 refinements"]
    click Ch4 "./input/search_terms/chapter_4.yaml" "Open chapter_4.yaml"

    subgraph Chapter4Sets["Chapter 4 searches"]
        direction TB
        C4_1["Set 1:<br/>Connectivity review articles"]
        C4_2["Set 2:<br/>Connectivity benefits vs risks<br/>+ ecosystem services"]
        C4_3["Set 3:<br/>Structural vs functional connectivity"]
        C4_4["Set 4:<br/>Connectivity modelling toolkits"]
        C4_5["Set 5:<br/>Connectivity indicators & metrics"]
        C4_6["Set 6:<br/>Policy & governance integration"]
        C4_7["Set 7:<br/>Multilevel / transboundary governance<br/>+ IPLC inclusion + land tenure"]
        C4_8["Set 8:<br/>Movement ecology<br/>(dispersal / migration / permeability)"]
    end
    Ch4 --> C4_1
    Ch4 --> C4_2
    Ch4 --> C4_3
    Ch4 --> C4_4
    Ch4 --> C4_5
    Ch4 --> C4_6
    Ch4 --> C4_7
    Ch4 --> C4_8

Chapter 5

flowchart LR
    Start([SPC Corpus]) --> Ch5L2["Chapter_5_1_2.yaml<br/>Level 2 foresight framing"]
    click Ch5L2 "./input/search_terms/Chapter_5_1_2.yaml" "Open Chapter_5_1_2.yaml"

    subgraph L2Sets["Section 5.1–5.2 searches"]
        direction TB
        C5_1["Set 1:<br/>Future + project*/predict*/scenario"]
        C5_2["Set 2:<br/>Future + pathway*/narrative*/vision*"]
        C5_3["Set 3:<br/>Future-proof*/anticipat*/scenario planning"]
        C5_4["Set 4:<br/>Foresight*/backcasting/simulat*/trend*"]
        C5_5["Set 5:<br/>Model* AND scenario"]
    end
    Ch5L2 --> C5_1
    Ch5L2 --> C5_2
    Ch5L2 --> C5_3
    Ch5L2 --> C5_4
    Ch5L2 --> C5_5

    Start --> Ch5L3["chapter_5_3.yaml<br/>Level 3 – Drivers of change"]
    click Ch5L3 "./input/search_terms/chapter_5_3.yaml" "Open chapter_5_3.yaml"
    subgraph L3Sets["Section 5.3 searches"]
        direction TB
        C5_3a["Set 1:<br/>Drivers of change"]
        C5_3b["Set 2:<br/>Driver modelling approaches"]
        C5_3c["Set 3:<br/>Driver gaps"]
    end
    Ch5L3 --> C5_3a
    Ch5L3 --> C5_3b
    Ch5L3 --> C5_3c

    Start --> Ch5L4["chapter_5_4.yaml<br/>Level 4 – Synergies & trade-offs"]
    click Ch5L4 "./input/search_terms/chapter_5_4.yaml" "Open chapter_5_4.yaml"
    subgraph L4Sets["Section 5.4 searches"]
        direction TB
        C5_4a["Set 1:<br/>Interactions (synergy/trade-off/nexus)"]
        C5_4b["Set 2:<br/>Response options (integrated planning / NbS)"]
        C5_4c["Set 3:<br/>Cross-scale synergy & trade-off terms"]
    end
    Ch5L4 --> C5_4a
    Ch5L4 --> C5_4b
    Ch5L4 --> C5_4c

    Start --> Ch5L5["chapter_5_5.yaml<br/>Level 5 – Uncertainty & risk"]
    click Ch5L5 "./input/search_terms/chapter_5_5.yaml" "Open chapter_5_5.yaml"
    subgraph L5Sets["Section 5.5 searches"]
        direction TB
        C5_5a["Set 1:<br/>Adaptive / transformative management"]
        C5_5b["Set 2:<br/>Uncertainty quantification"]
        C5_5c["Set 3:<br/>Tipping points & thresholds"]
        C5_5d["Set 4:<br/>Cascading risks & precaution"]
    end
    Ch5L5 --> C5_5a
    Ch5L5 --> C5_5b
    Ch5L5 --> C5_5c
    Ch5L5 --> C5_5d

    Start --> Ch5L6["chapter_5_6.yaml<br/>Level 6 – Knowledge to action"]
    click Ch5L6 "./input/search_terms/chapter_5_6.yaml" "Open chapter_5_6.yaml"
    subgraph L6Sets["Section 5.6 searches"]
        direction TB
        C5_6a["Set 1:<br/>Science-policy-practice pathways"]
        C5_6b["Set 2:<br/>ILK integration & community planning"]
        C5_6c["Set 3:<br/>Enabling factors & coordination"]
        C5_6d["Set 4:<br/>Shocks, surprises, uncertainties"]
    end
    Ch5L6 --> C5_6a
    Ch5L6 --> C5_6b
    Ch5L6 --> C5_6c
    Ch5L6 --> C5_6d

    Start --> Ch5CC["chapter_5_cc.yaml<br/>Cross-cutting themes"]
    click Ch5CC "./input/search_terms/chapter_5_cc.yaml" "Open chapter_5_cc.yaml"
    subgraph CCSets["Cross-cutting searches"]
        direction TB
        CC1["Set 1:<br/>Scales & telecoupling"]
        CC2["Set 2:<br/>Co-production & inclusion"]
    end
    Ch5CC --> CC1
    Ch5CC --> CC2

Chapter 6

flowchart LR
    Start([SPC Corpus]) --> Ch6["chapter_6.yaml<br/>Level 2 refinements"]
    click Ch6 "./input/search_terms/chapter_6.yaml" "Open chapter_6.yaml"

    subgraph Chapter6Sets["Chapter 6 searches"]
        direction TB
        C6_1["Set 1:<br/>Institutional & governance structures"]
        C6_2["Set 2:<br/>Political & strategic leadership"]
        C6_3["Set 3:<br/>Socio-cultural & stakeholder engagement"]
        C6_4["Set 4:<br/>Collaboration, trust & networks"]
        C6_5["Set 5:<br/>Financial & economic mechanisms"]
        C6_6["Set 6:<br/>Legal & policy frameworks"]
        C6_7["Set 7:<br/>Human & institutional capacity"]
        C6_8["Set 8:<br/>Data, knowledge & decision support"]
        C6_9["Set 9:<br/>Ecological & spatial planning tools"]
        C6_10["Set 10:<br/>Cross-cutting process enablers"]
    end
    Ch6 --> C6_1
    Ch6 --> C6_2
    Ch6 --> C6_3
    Ch6 --> C6_4
    Ch6 --> C6_5
    Ch6 --> C6_6
    Ch6 --> C6_7
    Ch6 --> C6_8
    Ch6 --> C6_9
    Ch6 --> C6_10

Ch6R2["chapter_6_r2.yaml<br/>Optional Level 3 filter"]
Chapter6Sets --> Ch6R2
click Ch6R2 "./input/search_terms/chapter_6_r2.yaml" "Open chapter_6_r2.yaml"
subgraph Chapter6Case["Chapter 6 searches"]
    R2["Case-study keywords<br/>(case stud*, example*, initiative*, etc.)"]
end
Ch6R2 --> R2

Type Selsection

OpenAlex contains more then 270 million works of different types. The following table shows and explains the available types and highlights which are selected to be included in the SPC Corpus.

params$types |>
  knitr::kable(
    caption = "OpenAlex Work Types and Inclusion in the SPC Corpus",
    booktabs = TRUE,
    align = c("l", "l", "l", "c")
  ) # |>

OpenAlex Work Types and Inclusion in the SPC Corpus

Type	Description	Included
article	Scholarly journal articles and related periodical works	TRUE
book	Monographs and other long-form published books	TRUE
book-chapter	Chapters published within edited books or proceedings	TRUE
dissertation	Doctoral or master level theses and dissertations	TRUE
editorial	Editorials and editor introductions	TRUE
preprint	Pre-publication manuscripts shared prior to peer review	TRUE
report	Technical, institutional, or policy reports	TRUE
review	Narrative or systematic review articles	TRUE
dataset	Published datasets and structured data releases	FALSE
erratum	Published corrections to previously released works	FALSE
grant	Summaries or descriptions of grant-funded projects	FALSE
letter	Correspondence, commentaries, and short letters	FALSE
libguides	Library research guides and curated bibliographies	FALSE
other	Works that do not align with a more specific OpenAlex type	FALSE
paratext	Prefaces, introductions, indexes, and other paratextual items	FALSE
peer-review	Formal peer review reports and evaluations	FALSE
reference-entry	Encyclopaedia or dictionary reference entries	FALSE
retraction	Notices retracting previously published works	FALSE
standard	Standards, protocols, and technical specifications	FALSE
supplementary-materials	Supplementary files accompanying primary publications	FALSE

# kableExtra::kable_styling(
#     full_width = FALSE,
#     position = "left"
# )

Methods

SPC Corpus

Assess of Individual Terms in spc and nature search terms

This assessment is done on the whole of the OpenAlex corpus and only filtered for types and not for the date range.

fn <- file.path(params$output_dir, "searchterm_assessment_spcc.rds")

if (!file.exists(fn)) {
  result <- list(
    spc = assess_search_term_both(
      st = params$search_terms$spc_corpus$spc,
      and_term = stcompact(params$search_terms$spc_corpus$spc_corpus$nature),
      types = params$types_filter,
      verbose = TRUE
    ),
    nature = assess_search_term_both(
      st = params$search_terms$spc_corpus$nature,
      and_term = stcompact(params$search_terms$spc_corpus$spc_corpus$spc),
      types = params$types_filter,
      verbose = FALSE
    )
  ) |>
    saveRDS(fn)
}

Get Key Paper

Here we get key papers in a parquet database which is partitioned by:

• found_in: the search term or openalex which is used as the filter, i.e. the key paper occurs in corpus which would result from the search term
• id_used: the id used to testing,m either the OpenAlex id (id) or the doi (doi)
• page: only for processing reasons

No filtering, neither by type nor by publication year is done.

The other columns are as returned by the OpenAlex API.

#|

st <- list(
  spc = params$search_terms$spc_corpus$spc |>
    paste0(collapse = " "),
  nature = params$search_terms$spc_corpus$nature |>
    paste0(collapse = " ")
)
st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

dois <- params$key_papers$goldstandard$DOI[
  params$key_papers$goldstandard$DOI != ""
]
ids <- params$key_papers$goldstandard$openalex_id[
  params$key_papers$goldstandard$openalex_id != ""
]

#|

fn <- file.path(params$keyworks, "parquet", "found_in=openalex")
if (!dir.exists(fn)) {
  ### KP in OpenAlex
  openalexPro::pro_query(
    doi = dois,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 50
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_oa_id")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=spc")
if (!dir.exists(fn)) {
  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(
      output = file.path(fn, "..", "json_doi")
    ) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "..", "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spc,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=nature")
if (!dir.exists(fn)) {
  ### KP in nature
  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    doi = dois,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$nature,
    id = ids,
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

#|

fn <- file.path(params$keyworks, "parquet", "found_in=spcc")
if (!dir.exists(fn)) {
  ### KP in spcc
  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    doi = params$key_papers$goldstandard$DOI[
      params$key_papers$goldstandard$DOI != ""
    ],
    chunk_limit = 15
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_doi")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_doi"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=doi"),
      delete_input = TRUE
    )

  openalexPro::pro_query(
    title_and_abstract.search = st$spcc,
    id = params$key_papers$goldstandard$openalex_id[
      params$key_papers$goldstandard$openalex_id != ""
    ],
    multiple_id = TRUE,
    chunk_limit = 25
  ) |>
    openalexPro::pro_request(output = file.path(fn, "json_oa_id")) |>
    openalexPro::pro_request_jsonl(
      output = file.path(fn, "jsonl_oa_id"),
      delete_input = TRUE
    ) |>
    openalexPro::pro_request_jsonl_parquet(
      output = file.path(fn, "id_used=oa_id"),
      delete_input = TRUE
    )
}

Keypaper in Search Terms

The in the previous step retrieved works are analysed here to get a table which shows where the key paper occur.

fn <- file.path(params$keyworks, "kp_found_in.rds")
if (!file.exists(fn)) {
  arrow::open_dataset(file.path(params$keyworks, "parquet")) |>
    dplyr::select(
      id,
      doi,
      type,
      found_in,
      title,
      citation
    ) |>
    dplyr::group_by(id, doi, title, citation, type) |>
    dplyr::summarise(
      in_openalex = base::max(found_in == "openalex", na.rm = TRUE),
      in_spc = base::max(found_in == "spc", na.rm = TRUE),
      in_nature = base::max(found_in == "nature", na.rm = TRUE),
      in_spcc = base::max(found_in == "spcc", na.rm = TRUE),
      .groups = "drop"
    ) |>
    dplyr::collect() |>
    saveRDS(fn)
}

Get Numbers from OpenAlex of the Search Terms

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, editorial, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

The following counts are retrieved:

Overall hits

#|

fn <- file.path(params$corpus, "st_hits.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc_corpus$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$spc_corpus$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Counts per Language

#|

fn <- file.path(params$corpus, "st_languages.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc_corpus$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$spc_corpus$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "language"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "language"
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, language = key_display_name, count) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fill = 0
    ) |>
    dplyr::select(
      language,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(language) |>
    saveRDS(file = fn)
}

Counts per Publication Year

fn <- file.path(params$corpus, "st_years.rds")
if (!file.exists(fn)) {
  st <- list(
    spc = params$search_terms$spc_corpus$spc |>
      paste0(collapse = " "),
    nature = params$search_terms$spc_corpus$nature |>
      paste0(collapse = " ")
  )
  st$spcc <- paste0("(", st$spc, ") AND (", st$nature, ")")

  queries <- lapply(
    st,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = s,
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to,
        group_by = "publication_year"
      )
    }
  )
  queries$openalex <- openalexPro::pro_query(
    type = params$types_filter,
    from_publication_date = params$publication_date$from,
    to_publication_date = params$publication_date$to,
    group_by = "publication_year"
  )

  result <- pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          verbose = TRUE
        ) |>
        dplyr::bind_rows()
    }
  ) |>
    dplyr::bind_rows(.id = "source") |>
    dplyr::select(source, year = key, count) |>
    dplyr::mutate(year = base::as.integer(year)) |>
    tidyr::pivot_wider(
      names_from = source,
      values_from = count,
      names_prefix = "count_",
      values_fill = 0
    ) |>
    dplyr::select(
      year,
      count_openalex,
      count_spc,
      count_nature,
      count_spcc
    ) |>
    dplyr::arrange(dplyr::desc(year)) |>
    saveRDS(file = fn)
}

Chapter 1

# params$search_terms$chapter_1

fn <- file.path(params$chapter_1, "st_hits.rds")
if (!file.exists(fn)) {
  spcc <- paste0("(", stcompact(params$search_terms$spc_corpus$spc), ") AND (", stcompact(params$search_terms$spc_corpus$nature), ")")
  queries <- lapply(
    params$search_terms$chapter_1,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = paste0("(", spcc, ") AND (", stcompact(s), ")"),
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Chapter 3

fn <- file.path(params$chapter_3, "st_hits.rds")
if (!file.exists(fn)) {
  spcc <- paste0("(", stcompact(params$search_terms$spc_corpus$spc), ") AND (", stcompact(params$search_terms$spc_corpus$nature), ")")
  queries <- lapply(
    params$search_terms$chapter_3,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = paste0("(", spcc, ") AND (", stcompact(s), ")"),
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Chapter 4

fn <- file.path(params$chapter_4, "st_hits.rds")
if (!file.exists(fn)) {
  spcc <- paste0("(", stcompact(params$search_terms$spc_corpus$spc), ") AND (", stcompact(params$search_terms$spc_corpus$nature), ")")
  queries <- lapply(
    params$search_terms$chapter_4,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = paste0("(", spcc, ") AND (", stcompact(s), ")"),
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Chapter 6

fn <- file.path(params$chapter_6, "st_hits.rds")
if (!file.exists(fn)) {
  spcc <- paste0("(", stcompact(params$search_terms$spc_corpus$spc), ") AND (", stcompact(params$search_terms$spc_corpus$nature), ")")
  queries <- lapply(
    params$search_terms$chapter_6,
    function(s) {
      openalexPro::pro_query(
        title_and_abstract.search = paste0("(", spcc, ") AND (", stcompact(s), ")"),
        type = params$types_filter,
        from_publication_date = params$publication_date$from,
        to_publication_date = params$publication_date$to
      )
    }
  )

  pbapply::pblapply(
    queries,
    function(query) {
      query |>
        openalexR::oa_request(
          count_only = TRUE,
          verbose = TRUE
        ) |>
        unlist()
    }
  ) |>
    do.call(what = cbind) |>
    t() |>
    as.data.frame() |>
    dplyr::select(count) |>
    saveRDS(file = fn)
}

Results

Assessment of Search Terms

The individual terms are assessed with the second term as AND, e,g. each individual term in spc is assessed with AND nature. In addition,

SPC Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$spc |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
(planning AND ("for ecosystem services" OR process OR scenario OR tools))	1,016,647	627,639
(scenario AND (analysis OR "based model" OR "based planning" OR building OR planning OR thinking OR "and models" OR "of land use"))	822,519	572,782
(spatial AND (composition OR configuration OR "conservation prioritisation" OR "conservation prioritization" OR decision OR development OR "forest planning" OR governance OR planning OR prioritisation OR prioritization OR transformation))	602,413	436,320
((anticipatory OR "community based" OR "forest management" OR "land-sea" OR participatory OR "place based" OR regional OR sectorial OR territorial OR urban OR "agricultural management") AND planning)	569,869	288,303
connectivity	461,758	421,047
restoration	461,290	414,027
(land AND (allocation OR governance OR system))	307,178	196,950
(adaptive AND (management OR planning))	300,667	197,005
(conservation AND (practice OR planning OR program OR strategy))	224,105	134,896
(("key biodiversity" OR protected OR "remote ocean") AND areas)	221,704	164,455
(landscape AND (complexity OR planning OR governance OR management OR "ecological planning"))	188,061	90,419
("land use" AND (decision OR planning OR governance OR management OR model OR scenario OR trade-offs OR tradeoffs))	175,717	61,628
((integrative OR "trends and") AND scenarios)	157,786	44,471
(animal AND (migration OR movement))	69,677	59,417
((functional OR working) AND landscapes)	57,262	33,433
(("cumulative impact" OR "environmental impact" OR "strategic environmental") AND assessment)	53,022	39,824
(ecological AND (corridor OR networks))	48,448	28,849
zoning	47,470	34,068
"systems integration"	45,355	39,505
(biodiversity AND (assessment OR indicators OR scenario))	42,857	18,865
((blue OR green) AND infrastructure)	34,200	21,547
("ecosystem service" AND (mapping OR model OR planning))	23,141	5,925
((inclusive OR marine) AND governance)	22,645	14,496
"stepping stones"	15,421	13,466
(nature AND ("based solutions" OR futures))	15,025	10,555
(integrated AND ("assessment framework" OR "assessment model" OR "coastal zone management"))	9,028	5,531
((habitat OR wildlife) AND corridor)	7,986	2,479
(("ecosystem based" OR "sea use") AND management)	5,700	2,989
"multi-criteria decision analysis"	5,568	4,062
rewilding	2,048	1,414
(("generalized dissimilarity" OR macroecological) AND model)	1,051	657
IPBES	1,011	577
"reserve design"	640	315
"futures thinking"	554	388
OECM	325	201
"critical areas for biodiversity"	17	7

Nature Term

readRDS(file.path(params$output, "searchterm_assessment_spcc.rds"))$nature |>
  dplyr::arrange(desc(count)) |>
  dplyr::mutate(
    count = format(count, big.mark = ","),
    count_excl = format(count_excl, big.mark = ","),
  ) |>
  knitr::kable(format = "html", escape = FALSE)

term	count	count_excl
environment	5,628,345	4,169,826
species	3,615,729	2,254,923
nature	3,236,060	2,485,363
sustainable	2,901,442	2,070,778
soil	1,900,934	1,250,549
sea	1,251,679	753,939
forest	1,056,247	528,538
river	1,014,129	576,820
ecological	894,562	307,329
landscape	886,301	516,869
conservation	827,750	371,991
Earth	816,853	536,257
ecosystem	757,569	204,087
marine	732,912	345,603
ocean	638,250	315,888
habitat	526,700	129,189
lake	502,996	292,091
restoration	461,290	310,633
mountain	436,824	226,862
coast	405,029	186,587
biodiversity	274,104	49,065
terrestrial	250,166	89,718
flora	203,586	106,716
planet	198,843	107,449
freshwater	197,657	68,682
fauna	177,738	54,737
maritime	165,508	100,528
plantation	156,225	74,660
desert	148,279	80,520
wetland	142,734	55,441
wildlife	140,384	44,400
grassland	115,594	31,832
estuary	91,764	30,842
bog	63,942	47,301
marshes	62,247	27,301
"protected areas"	58,787	10,640
"natural resource"	57,705	23,479
"agricultural land"	52,808	19,771
meadow	50,125	15,737
woodland	48,886	13,751
biosphere	42,260	11,825
"coastal waters"	32,463	10,831
savanna	28,058	8,946
"coupled system"	24,957	20,583
dryland	24,491	9,880
peatland	20,474	8,506
"arable land"	18,026	4,736
mires	17,488	11,466
tundra	17,263	4,695
fjord	13,064	5,973
shrubland	9,550	1,171
bioeconomy	8,433	4,195
"resource system"	4,983	2,905
seascape	4,297	1,132
heathland	3,323	833
marshland	3,312	898
chaparral	2,662	1,168
"environmental resource"	2,071	735
agroforest	1,498	322
"agro-forest"	478	0

Keypaper in Corpus

No filtering, neither by type nor by publication year is done. Therefore, the pure search terms are evaluated. If a paper is included in this table, doex not mean it is included in the final SPC Corpus due to filtering by dates and types!

readRDS(file.path(params$keyworks, "kp_found_in.rds")) |>
  dplyr::mutate(
    id_display = sub("^.*/(W[0-9]+)$", "\\1", id),
    id = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", id, id_display),
    doi_display = sub("^https://doi.org/", "\\1", doi),
    doi = sprintf("<a href=\"%s\" target=\"_blank\">%s</a>", doi, doi_display)
  ) |>
  dplyr::arrange(
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("in_"),
      ~ dplyr::case_when(
        .x ~ '<b style="color:#008000;">☑</b>', # green bold checkbox
        !.x ~ '<b style="color:#cc0000;">☐</b>' # red bold empty checkbox
      )
    )
  ) |>
  dplyr::select(
    id,
    doi,
    citation,
    in_spcc,
    in_spc,
    in_nature,
    in_openalex
  ) |>
  knitr::kable(format = "html", escape = FALSE)

id	doi	citation	in_spcc	in_spc	in_nature	in_openalex
W2128217744	10.1177/0306312713508669	David H. Guston (2013)	☐	☐	☐	☑
W1973628253	10.1016/j.enpol.2005.12.006	Will McDowall & Malcolm Eames (2006)	☐	☐	☐	☑
W3170399214	10.1016/j.mex.2021.101401	Daniel Beiderbeck et al. (2021)	☐	☐	☐	☑
W2964311550	10.1016/j.techfore.2019.07.002	Ian Belton et al. (2019)	☐	☐	☐	☑
W2015646994	10.1016/j.tree.2014.07.005	Carly N. Cook et al. (2014)	☐	☐	☐	☑
W4238224782	10.1016/j.futures.2015.08.007	I. Milojević & Sohail Inayatullah (2015)	☐	☐	☐	☑
W2086673960	10.1016/j.tree.2009.04.008	William J. Sutherland & Harry J. Woodroof (2009)	☐	☐	☐	☑
W4233598570	10.1146/annurev-anthro-102218-011435	David Valentine & Amelia Hassoun (2019)	☐	☐	☐	☑
W2056591496	10.1016/s0016-3287(98)00101-3	Richard A. Slaughter (1998)	☐	☐	☐	☑
W2090312322	10.1016/j.futures.2007.11.010	Joseph Voros (2007)	☐	☐	☐	☑
W1992974326	10.1016/j.futures.2007.11.011	Richard A. Slaughter (2007)	☐	☐	☐	☑
W2795786882	10.1007/s00267-018-1028-3	Ida N.S. Djenontin & Alison M. Meadow (2018)	☐	☐	☐	☑
W2904898541	10.1007/s10668-018-00300-5	Azime Tezer et al. (2018)	☐	☐	☑	☑
W1990241575	10.1007/s10021-004-0074-2	Paul Raskin (2005)	☐	☐	☑	☑
W4414957974	10.1073/pnas.2501695122	Damaris Zurell et al. (2025)	☐	☐	☑	☑
W4298615974	10.1007/s10980-022-01534-5	Tom Harwood et al. (2022)	☐	☐	☑	☑
W4210765186	10.1038/s41893-021-00844-x	Roslyn Henry et al. (2022)	☐	☐	☑	☑
W2910481941	10.1080/02513625.2018.1562795	Peter Schmitt & Thorsten Wiechmann (2018)	☐	☑	☐	☑
W2116090915	10.1016/j.futures.2012.10.003	Muhammad Amer et al. (2012)	☐	☑	☐	☑
W2057938345	10.1108/14636680810855991	Sohail Inayatullah (2008)	☐	☑	☐	☑
W4386609980	10.1038/s43588-023-00503-5	Yu Zheng et al. (2023)	☐	☑	☐	☑
W4407294089	10.1080/02697459.2025.2459975	Romina Rodela (2025)	☐	☑	☐	☑
W2265414043	10.1146/annurev.ecolsys.32.081501.114012	Steward T. A. Pickett et al. (2001)	☑	☑	☑	☑
W2918948909	10.1016/j.gecco.2019.e00569	Arieanna C. Balbar & Anna Meta×as (2019)	☑	☑	☑	☑
W2099188808	10.1016/j.landurbplan.2006.04.005	Jolande W. Termorshuizen et al. (2006)	☑	☑	☑	☑
W4297536966	10.1016/j.tree.2022.09.002	Maria Beger et al. (2022)	☑	☑	☑	☑
W2027491594	10.1016/j.ecolind.2015.03.029	Christian Albert et al. (2015)	☑	☑	☑	☑
W2766942546	10.1080/08920753.2017.1373450	Kekuewa Kikiloi et al. (2017)	☑	☑	☑	☑
W3194313759	10.1007/978-94-024-1681-7	Christina von Haaren et al. (2019)	☑	☑	☑	☑
W1966247992	10.1080/21513732.2011.617711	Davide Geneletti (2011)	☑	☑	☑	☑
W3100416804	10.1111/1365-2664.13796	Virgilio Hermoso et al. (2020)	☑	☑	☑	☑
W4409833817	10.1126/science.adn2225	Jedediah F. Brodie et al. (2025)	☑	☑	☑	☑
W2884329716	10.1007/978-3-319-94021-2	László Miklós & Anna Špinerová (2018)	☑	☑	☑	☑
W2524916285	10.1002/aqc.2645	Alan M. Friedlander et al. (2016)	☑	☑	☑	☑
W3021127236	10.1016/j.marpol.2020.103950	Thomas Robertson et al. (2020)	☑	☑	☑	☑
W4413900892	10.1016/j.tree.2025.07.014	Jian Peng et al. (2025)	☑	☑	☑	☑
W4412750811	10.1016/j.marpol.2025.106852	Jean‐Eudes Beuret et al. (2025)	☑	☑	☑	☑
W4410629764	10.1016/j.rsma.2025.104257	Liisi Lees et al. (2025)	☑	☑	☑	☑
W4281717750	10.1126/science.abl8974	Angela Brennan et al. (2022)	☑	☑	☑	☑
W1963746476	10.1016/j.ecocom.2009.10.006	R.S. de Groot et al. (2009)	☑	☑	☑	☑
W4211243502	10.1038/35012251	Chris Margules & Robert L. Pressey (2000)	☑	☑	☑	☑
W2759207970	10.3390/su9091668	Leena Karrasch et al. (2017)	☑	☑	☑	☑
W3159060995	10.1016/j.ecoser.2021.101273	Karsten Grunewald et al. (2021)	☑	☑	☑	☑
W2487200415	10.1016/j.marpol.2016.06.023	Elianny Domínguez-Tejo et al. (2016)	☑	☑	☑	☑
W2956763155	10.1088/1748-9326/ab3234	Annika T. H. Keeley et al. (2019)	☑	☑	☑	☑
W3200717663	10.1007/s10980-021-01329-0	Jianquan Dong et al. (2021)	☑	☑	☑	☑
W4313593374	10.1016/j.mex.2022.101989	Holly Kirk et al. (2023)	☑	☑	☑	☑
W4387055605	10.1038/s44183-023-00022-w	Julie Reimer et al. (2023)	☑	☑	☑	☑
W3215595594	10.1093/biosci/biab091	Abigail J. Lynch et al. (2021)	☑	☑	☑	☑
W4393866290	10.3390/su16072965	Qiqi Hu et al. (2024)	☑	☑	☑	☑
W2554309037	10.1111/btp.12386	Agnieszka E. Latawiec et al. (2016)	☑	☑	☑	☑
W3209482460	10.25607/obp-1666	Alejandro Iglesias-Campos et al. (2021)	☑	☑	☑	☑
W4320016094	10.1007/978-3-031-15773-8_4	Falko Buschke et al. (2023)	☑	☑	☑	☑
W4295308933	10.1073/pnas.2203385119	Natalia Estrada-Carmona et al. (2022)	☑	☑	☑	☑
W4412257196	10.5281/zenodo.6522392	Unai Pascual et al. (2022)	☑	☑	☑	☑
W4415164578	10.1098/rsos.250810	Rachael Garrett et al. (2025)	☑	☑	☑	☑
W2114666631	10.1525/bio.2010.60.3.7	Timothy J. Beechie et al. (2010)	☑	☑	☑	☑
W2612157793	10.1016/j.marpol.2017.06.020	Mara Ntona & Elisa Morgera (2017)	☑	☑	☑	☑
W2035832288	10.1007/s10980-014-0085-0	Christian Albert et al. (2014)	☑	☑	☑	☑
W2604803374	10.1093/biosci/bix012	Charles H. Nilon et al. (2017)	☑	☑	☑	☑
W4220936177	10.1016/j.oneear.2022.02.008	Christopher M. Raymond et al. (2022)	☑	☑	☑	☑
W2999493939	10.1016/j.landurbplan.2019.103741	Christian Albert et al. (2020)	☑	☑	☑	☑
W2766457534	10.1016/j.landusepol.2017.10.017	Chiara Cortinovis & Davide Geneletti (2017)	☑	☑	☑	☑
W4376627395	10.1016/j.marpol.2023.105655	Julie Reimer et al. (2023)	☑	☑	☑	☑
W4211108983	10.1007/978-3-030-20024-4	Davide Geneletti et al. (2019)	☑	☑	☑	☑
W2804302736	10.1016/j.scitotenv.2018.05.147	Maria da Luz Fernandes et al. (2018)	☑	☑	☑	☑
W3134065015	10.1016/j.envsci.2021.02.001	Davide Longato et al. (2021)	☑	☑	☑	☑
W4392293717	10.1016/j.ecolind.2024.111816	Wen Song et al. (2024)	☑	☑	☑	☑
W2046569818	10.1007/s10980-014-0052-9	Christine Fürst et al. (2014)	☑	☑	☑	☑
W2810876831	10.3897/rio.4.e28045	Evelyn Underwood et al. (2018)	☑	☑	☑	☑
W2902345284	10.1007/s10980-018-0745-6	Marcin Spyra et al. (2018)	☑	☑	☑	☑
W3158973852	10.1016/j.landurbplan.2021.104129	Chiara Cortinovis et al. (2021)	☑	☑	☑	☑
W2771003204	10.1080/21513732.2017.1396257	Christine Fürst et al. (2017)	☑	☑	☑	☑
W2592317409	10.1080/21513732.2017.1296494	Justice Nana Inkoom et al. (2017)	☑	☑	☑	☑
W4406477943	10.1007/s11252-024-01656-5	Israa H. Mahmoud et al. (2025)	☑	☑	☑	☑
W4289516997	10.1007/s41207-022-00315-5	Georgia Pozoukidou et al. (2022)	☑	☑	☑	☑
W4404646473	10.1007/s00267-024-02086-x	Nina Farwig et al. (2024)	☑	☑	☑	☑
W2161139387	10.1126/science.1196624	Henrique M. Pereira et al. (2010)	☑	☑	☑	☑
W2023339029	10.1046/j.1523-1739.2003.01491.x	Garry Peterson et al. (2003)	☑	☑	☑	☑
W2971398159	10.1111/rec.13035	George D. Gann et al. (2019)	☑	☑	☑	☑
W2062482344	10.1073/pnas.1201040109	Joshua Goldstein et al. (2012)	☑	☑	☑	☑
W1540682446	10.1111/brv.12008	Aija S. Kukkala & Atte Moilanen (2012)	☑	☑	☑	☑
W2028797766	10.1016/j.cosust.2013.05.002	Ralf Seppelt et al. (2013)	☑	☑	☑	☑
W2470861343	10.1016/j.landurbplan.2016.05.003	Adrienne Grêt‐Regamey et al. (2016)	☑	☑	☑	☑
W2754686867	10.1038/s41559-017-0273-9	Isabel M.D. Rosa et al. (2017)	☑	☑	☑	☑
W3185562566	10.1038/d41586-021-02041-4	Georgina G. Gurney et al. (2021)	☑	☑	☑	☑
W4395447577	10.1126/science.adn3441	Henrique M. Pereira et al. (2024)	☑	☑	☑	☑
W4212886831	10.1111/geb.13459	Karel Mokany et al. (2022)	☑	☑	☑	☑
W4406904698	10.1016/j.tree.2024.12.002	Sylvaine Giakoumi et al. (2025)	☑	☑	☑	☑
W4406487214	10.1007/s10980-024-02042-4	Jiangxiao Qiu et al. (2025)	☑	☑	☑	☑
W2015619301	10.1073/pnas.1000530107	Lian Pin Koh & Jaboury Ghazoul (2010)	☑	☑	☑	☑
W3126018057	10.1111/rec.13346	Jordi Cortina et al. (2021)	☑	☑	☑	☑
W2156111479	10.1111/gcb.12383	Sebastián Martinuzzi et al. (2013)	☑	☑	☑	☑
W2902463689	10.1016/j.tree.2018.10.006	Emily Nicholson et al. (2018)	☑	☑	☑	☑
W2952896657	10.5194/gmd-11-4537-2018	Hyejin Kim et al. (2018)	☑	☑	☑	☑
W3161819570	10.1126/science.abc4896	Louise O’Connor et al. (2021)	☑	☑	☑	☑
W4382181311	10.1007/s11625-023-01316-1	América Paz Durán et al. (2023)	☑	☑	☑	☑
W2769947232	10.1016/j.cosust.2017.10.004	Jean Paul Metzger et al. (2017)	☑	☑	☑	☑
W3151200781	10.1111/rec.13403	Ben L. Gilby et al. (2021)	☑	☑	☑	☑
W56780107	10.1007/978-1-4612-0529-6_10	Jack Ahern (1999)	☑	☑	☑	☑
W2159760863	10.1111/j.1466-8238.2010.00620.x	Joachim H. Spangenberg et al. (2011)	☑	☑	☑	☑
W4382751812	10.1016/j.biocon.2023.110068	Marcel Kok et al. (2023)	☑	☑	☑	☑
W3044450114	10.1016/j.envsoft.2020.104806	Andrew J. Hoskins et al. (2020)	☑	☑	☑	☑
W3074014079	10.1007/s10113-020-01685-8	Clara J. Veerkamp et al. (2020)	☑	☑	☑	☑
W3145276257	10.1016/j.jenvman.2021.112400	Yuyoung Choi et al. (2021)	☑	☑	☑	☑
W2614376759	10.1016/j.envsci.2017.05.003	Vanessa M. Adams et al. (2017)	☑	☑	☑	☑
W4386734502	10.1146/annurev-environ-112321-095011	Steven J. Cork et al. (2023)	☑	☑	☑	☑
W2073677603	10.1016/j.biocon.2015.02.015	Sebastián Martinuzzi et al. (2015)	☑	☑	☑	☑
W2972648221	10.1111/1365-2664.13506	Karel Mokany et al. (2019)	☑	☑	☑	☑
W4411717980	10.1007/s11625-025-01682-y	Sana Okayasu et al. (2025)	☑	☑	☑	☑
W4310004272	10.1007/s11625-022-01251-7	Lucas Rutting et al. (2022)	☑	☑	☑	☑
W1990273831	10.1126/science.1242552	Silke Bauer & Bethany J. Hoye (2014)	☑	☑	☑	☑
W2784805535	10.1126/science.aam9712	Marlee A. Tucker et al. (2018)	☑	☑	☑	☑
W3085078608	10.1038/s41467-020-18457-x	Michelle Ward et al. (2020)	☑	☑	☑	☑
W1880093763	10.1111/ecog.01507	Rafael A. Magris et al. (2015)	☑	☑	☑	☑
W2783683111	10.1016/j.biocon.2017.12.020	Santiago Saura et al. (2018)	☑	☑	☑	☑
W2791599583	10.1111/conl.12439	Rafael A. Magris et al. (2018)	☑	☑	☑	☑
W2965201645	10.1016/j.biocon.2019.07.028	Santiago Saura et al. (2019)	☑	☑	☑	☑
W3132073286	10.1016/j.biocon.2021.109008	Annika T. H. Keeley et al. (2021)	☑	☑	☑	☑
W4390696637	10.1038/s41467-023-43832-9	Rachel Neugarten et al. (2024)	☑	☑	☑	☑
W4302293680	10.1111/csp2.12823	David M. Theobald et al. (2022)	☑	☑	☑	☑
W4402144060	10.1002/ece3.70231	Amanda Liczner et al. (2024)	☑	☑	☑	☑
W4412704230	10.1073/pnas.2410937122	Robin Naidoo et al. (2025)	☑	☑	☑	☑
W4280541891	10.3389/fevo.2022.830822	Sylvia Wood et al. (2022)	☑	☑	☑	☑
W4392367143	10.1111/brv.13066	Steven J. Cooke et al. (2024)	☑	☑	☑	☑
W4410131928	10.3354/meps14888	Susanne E. Tanner et al. (2025)	☑	☑	☑	☑
W4413449508	10.1038/s41467-025-63205-8	Jedediah F. Brodie et al. (2025)	☑	☑	☑	☑
W4414925393	10.1016/j.tree.2025.09.007	Sandra Neubert et al. (2025)	☑	☑	☑	☑
W2255223904	NA	Bob Scholes (2010)	☑	☑	☑	☑
W2978599153	NA	Peter H. Verburg et al. (2019)	☑	☑	☑	☑
W4415240687	10.1007/s10980-025-02210-0	Tamsin L. Woodman et al. (2025)	☑	☑	☑	☑
W3085006993	10.1002/pan3.10146	Laura Pereira et al. (2020)	☑	☑	☑	☑
W2093445010	10.1126/science.1258832	Jianguo Liu et al. (2015)	☑	☑	☑	☑
W4380362703	10.1016/j.gloenvcha.2023.102681	Hyejin Kim et al. (2023)	☑	☑	☑	☑
W4210386268	10.1016/j.envsci.2022.01.013	Andressa V. Mansur et al. (2022)	☑	☑	☑	☑
W2122104680	10.1111/j.1523-1739.2009.01212.x	William J. Sutherland et al. (2009)	☑	☑	☑	☑

SPC Corpus Measures and Numbers

These data is gathered from OpenAlex directly, not downloaded any works. The data is used to assess the quality of the TCA Corpus.

The query contains: - the search term (nature, spc, spcc) - the types selected (article, book, book-chapter, dissertation, editorial, preprint, report, review) - the date range (from 1992-01-01 to 2025-12-31)

Overall counts

readRDS(file.path(params$corpus, "st_hits.rds")) |>
  dplyr::mutate(
    count = format(count, big.mark = ",")
  ) |>
  knitr::kable(format = "html", escape = FALSE)

	count
spc	4,540,713
nature	20,174,128
spcc	2,220,221
openalex	206,359,476

Publication Years

readRDS(file.path(params$corpus, "st_years.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

year	count_openalex	count_spc	count_nature	count_spcc
2025	6,329,647	269,512	963,820	153,565
2024	8,670,872	344,987	1,265,778	184,319
2023	8,738,795	336,209	1,234,064	167,936
2022	7,788,332	286,117	1,094,753	140,819
2021	8,727,338	289,326	1,116,499	139,635
2020	9,568,427	275,312	1,089,285	131,475
2019	9,309,954	234,536	980,923	110,162
2018	9,088,145	213,301	910,018	100,204
2017	9,036,442	194,774	846,609	90,810
2016	9,223,797	188,189	824,278	88,225
2015	8,995,848	184,968	815,518	87,337
2014	8,903,032	180,964	819,518	86,340
2013	8,629,314	171,217	790,683	81,682
2012	8,166,331	156,660	741,051	75,505
2011	7,898,106	146,247	695,938	70,195
2010	7,364,775	132,282	651,249	63,836
2009	6,892,547	116,420	586,378	55,990
2008	6,377,760	104,273	526,909	49,988
2007	5,927,238	91,643	481,199	44,084
2006	5,588,856	85,041	447,241	40,650
2005	5,119,892	75,496	404,987	36,302
2004	4,686,841	65,745	363,301	32,031
2003	4,356,515	59,870	335,471	28,914
2002	4,165,034	58,792	327,544	27,727
2001	3,574,270	42,437	260,635	20,958
2000	3,406,355	38,876	247,224	19,021
1999	2,957,468	32,716	207,587	15,798
1998	2,819,905	29,627	195,158	14,253
1997	2,690,018	27,100	183,103	12,911
1996	2,561,448	25,907	177,272	12,022
1995	2,385,363	23,621	163,935	11,050
1994	2,248,063	21,393	150,096	9,806
1993	2,132,163	19,446	143,412	8,709
1992	2,030,586	17,709	132,692	7,962

This graph only shows the relative nuymber of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_years.rds")) |>
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale each source so its total sum is 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_count = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_count > 0, count / total_count, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_count) |>
  ggplot2::ggplot(ggplot2::aes(x = year, y = count, color = source)) +
  ggplot2::geom_line(linewidth = 1) +
  ggplot2::geom_point(size = 1.5) +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0.02, 0.06))
  ) +
  ggplot2::labs(
    x = "Year",
    y = "Share of total works (sum-scaled per source)",
    color = "Source",
    title = "Publications per year by source (each source sums to 1)"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )

Language

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  dplyr::mutate(
    dplyr::across(
      dplyr::starts_with("count_"),
      ~ base::format(.x, big.mark = ",")
    )
  ) |>
  knitr::kable(format = "html", escape = FALSE)

language	count_openalex	count_spc	count_nature	count_spcc
Afrikaans	152,064	186	1,881	85
Albanian	13,463	5	159	3
Arabic	629,690	739	4,002	316
Bengali	2,831	5	59	4
Bulgarian	52,160	119	805	61
Catalan	548,816	1,374	9,242	658
Chinese	4,514,153	620	9,036	275
Croatian	327,752	571	5,455	234
Czech	411,672	148	1,491	86
Danish	272,371	215	4,474	54
Dutch	760,453	718	6,090	223
English	147,226,131	4,412,876	19,339,969	2,170,343
Estonian	107,444	111	1,518	60
Finnish	172,580	175	1,183	59
French	5,683,321	19,313	194,313	7,893
German	5,126,898	5,530	25,172	1,709
Gujarati	433	0	3	0
Hebrew	3,853	24	82	13
Hindi	10,137	2	27	0
Hungarian	132,392	546	2,705	263
Indonesian	3,065,335	30,700	130,878	10,606
Italian	1,485,985	2,461	15,210	1,275
Japanese	6,356,671	1,253	10,635	448
Kannada	384	0	0	0
Korean	3,773,377	1,502	12,173	555
Latvian	25,364	13	126	8
Lithuanian	95,950	362	1,198	189
Macedonian	27,117	48	215	14
Malayalam	308	0	14	0
Marathi	2,225	1	8	1
Modern Greek (1453-)	139,882	185	1,215	71
Nepali (macrolanguage)	3,418	6	59	3
Norwegian	252,659	141	2,123	35
Panjabi	118	0	1	0
Persian	493,319	192	2,037	75
Polish	1,119,116	604	5,555	251
Portuguese	3,992,720	7,622	52,248	4,052
Romanian	259,537	252	3,855	115
Russian	2,267,290	11,177	50,456	5,514
Slovak	51,243	38	281	18
Slovenian	104,086	189	1,297	98
Somali	19,578	6	77	4
Spanish	7,150,614	33,028	208,043	11,534
Swahili (macrolanguage)	24,088	5	145	1
Swedish	431,008	426	2,087	86
Tagalog	90,423	98	1,093	13
Tamil	3,050	4	108	1
Telugu	64	0	2	0
Thai	95,822	2,155	4,062	708
Turkish	868,717	986	7,453	516
Ukrainian	508,196	1,287	8,113	585
Urdu	2,608	0	4	0
Vietnamese	164,519	159	1,535	88
Welsh	42,954	8	152	2
NA	26,847	2	30	0

This graph only shows the relative number of publications per year to identify different trends.

readRDS(file.path(params$corpus, "st_languages.rds")) |>
  # keep top 15 languages by total (before scaling)
  dplyr::mutate(
    total = count_openalex + count_spc + count_nature + count_spcc
  ) |>
  dplyr::slice_max(total, n = 15) |>
  dplyr::arrange(dplyr::desc(total)) |>
  # fix display order so largest stays on top
  dplyr::mutate(language = factor(language, levels = rev(language))) |>
  dplyr::select(-total) |>
  # reshape wide → long
  tidyr::pivot_longer(
    cols = dplyr::starts_with("count_"),
    names_to = "source",
    values_to = "count",
    names_prefix = "count_"
  ) |>
  # scale so each source sums to 1
  dplyr::group_by(source) |>
  dplyr::mutate(
    total_source = base::sum(count, na.rm = TRUE),
    count = dplyr::if_else(total_source > 0, count / total_source, 0)
  ) |>
  dplyr::ungroup() |>
  dplyr::select(-total_source) |>
  ggplot2::ggplot(ggplot2::aes(x = language, y = count, fill = source)) +
  ggplot2::geom_col(position = "dodge") +
  ggplot2::coord_flip() +
  ggplot2::scale_y_continuous(
    labels = scales::label_percent(accuracy = 1),
    expand = ggplot2::expansion(mult = c(0, 0.05))
  ) +
  ggplot2::labs(
    x = "Language",
    y = "Share of total works within each source",
    fill = "Source",
    title = "Publications by language (top 15), scaled so each source sums to 1"
  ) +
  ggplot2::theme_minimal(base_size = 14) +
  ggplot2::theme(
    legend.position = "bottom",
    panel.grid.minor = ggplot2::element_blank()
  )

Chapter 1

readRDS("spc_corpus/output/chapter_1/st_hits.rds") |> 
  dplyr::arrange(count)

Chapter 3

Term 1 is to long

params$search_terms$chapter_3 |> sapply(FUN = nchar) |> sapply(FUN = sum)

  1   2   3   4   5   6   7   8   9  10 
529 523 191 201 133 308  47 123 176 176 

# readRDS("spc_corpus/output/chapter_3/st_hits.rds") |> 
#   dplyr::arrange(count)

Chapter 4

readRDS("spc_corpus/output/chapter_4/st_hits.rds") |> 
  dplyr::arrange(count)

Chapter 6

Term 8 is to long

params$search_terms$chapter_6 |> sapply(FUN = nchar) |> sapply(FUN = sum)

     1) Institutional & Governance Structures 
                                          277 
          2) Political & Strategic Leadership 
                                          138 
   3) Socio-Cultural & Stakeholder Engagement 
                                          278 
           4) Collaboration, Trust & Networks 
                                          135 
           5) Financial & Economic Mechanisms 
                                          179 
                 6) Legal & Policy Frameworks 
                                          152 
            7) Human & Institutional Capacity 
                                          345 
8) Data, Knowledge & Decision-Support Systems 
                                          893 
            9) Ecologic al & Spatial Planning 
                                          387 
           10) Cross-Cutting Process Enablers 
                                          129 

# readRDS("spc_corpus/output/chapter_6/st_hits.rds") |> 
#   dplyr::arrange(count)